A comparison of denoising pipelines in high temporal resolution task‐based functional magnetic resonance imaging data

• Published in: Human brain mapping Vol. 40; no. 13; pp. 3843 - 3859
• Main Authors: Mayer, Andrew R., Ling, Josef M., Dodd, Andrew B., Shaff, Nicholas A., Wertz, Christopher J., Hanlon, Faith M.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2019
• Subjects: Adolescent; Adult; Algorithms; Aroma; Brain - diagnostic imaging; Brain - physiology; denoising; Design; Female; fMRI; Functional magnetic resonance imaging; Functional Neuroimaging - methods; Functional Neuroimaging - standards; Head; head motion; Head movement; Head Movements; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; NMR; Noise; Noise reduction; Nuclear magnetic resonance; Parameters; Pattern Recognition, Visual - physiology; Pipelines; Psychomotor Performance - physiology; Quality control; Regression analysis; Research Design; Resonance; signal‐to‐noise; Statistical analysis; Temporal resolution; Young Adult; denoising; fMRI; head motion; signal-to-noise; artifacts
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.24635

It has been known for decades that head motion/other artifacts affect the blood oxygen level‐dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind‐source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event‐related functional magnetic resonance imaging (erfMRI) and block‐design (bdfMRI) datasets collected with multiband 32‐ (repetition time [TR] = 460 ms) or older 12‐channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind‐source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind‐source denoising approaches appear to remove signal as well as noise from task‐related data at individual subject and group levels.